We use a ResNet34 [1]} encoder, which outputs \(r\) , a 512D representation.
As compactness is a prerequisite for disentanglement (i.e.,  a factor of variation is represented with minimal elements) and our analysis finds our regularizers have a local effect on the representation (see Section REF ), we also try using the projection output \(z\)  as input to the linear classifier \(C\)  used for our downstream tasks.
